This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility (Vis) and ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are also functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in-situ observations at the surface and in the cloud, as well as aircraft and Unmanned Aerial Vehicles (UAV) mounted sensors, are becoming more common. Because of prediction issues at smaller time and space scales (e.g., <1 km), meteorological forecasts from NWP models need to be continuously improved. Aviation weather forecasts also need to be developed to provide information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.

Assigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.

Current state-of-the art regional numerical weather forecasts are run at horizontal grid spacings of a few kilometers, which permits medium to large-scale convective systems to be represented explicitly in the model. With the convection parameterization no longer active, much uncertainty in the formulation of subgrid-scale processes moves to other areas such as the cloud microphysical, turbulence, and land-surface parameterizations. The goal of this study is to investigate experiments with stochastically-perturbed parameters (SPP) within a microphysics parameterization and the model’s horizontal diffusion coefficients. To estimate the “true” uncertainty due to parameter uncertainty, the magnitudes of the perturbations are chosen as realistic as possible and not with purposeful intent of maximal forecast impact as some prior work has done. Spatial inhomogeneities and temporal persistence are represented using a random perturbation pattern with spatial and temporal correlations. The impact on the distributions of various hydrometeors, precipitation characteristics, and solar/longwave radiation are quantified for a winter and summer case. In terms of upscale error growth, the impact is relatively small and consists primarily of triggering atmospheric instabilities in convectively unstable regions. In addition, small in situ changes with potentially large socio-economic impacts are observed in the precipitation characteristics such as maximum hail size. Albeit the impact of introducing physically-based parameter uncertainties within the bounds of aerosol uncertainties is small, their influence on the solar and longwave radiation balances may still have important implications for global model simulations of future climate scenarios.

This paper introduces a method of image filtering for viewing gravity waves in satellite imagery, which is particularly timely to the advent of the next-generation Advanced Himawari Imager (AHI) and the Advanced Baseline Imager (ABI). Applying a “high pass” filter to the upper-troposphere water vapor channel reveals sub-Kelvin-degree variations in brightness temperature that depict an abundance of gravity wave activity at the AHI/ABI sensitivity. Three examples demonstrate that this high-pass product can be exploited in a forecasting setting to identify possible varieties of turbulence-prone gravity waves that either 1) move roughly orthogonally to the apparent background flow or 2) produce interference as separate wave packets pass through the same location.

In this study, the utility of dimensioned, neighborhood-based, and object-based forecast verification metrics for cloud verification is assessed using output from the experimental High Resolution Rapid Refresh (HRRRx) model over a 1-day period containing different modes of convection. This is accomplished by comparing observed and simulated Geostationary Operational Environmental Satellite (GOES) 10.7-μm brightness temperatures (BTs). Traditional dimensioned metrics such as mean absolute error (MAE) and mean bias error (MBE) were used to assess the overall model accuracy. The MBE showed that the HRRRx BTs for forecast hours 0 and 1 are too warm compared with the observations, indicating a lack of cloud cover, but rapidly become too cold in subsequent hours because of the generation of excessive upper-level cloudiness. Neighborhood and object-based statistics were used to investigate the source of the HRRRx cloud cover errors. The neighborhood statistic fractions skill score (FSS) showed that displacement errors between cloud objects identified in the HRRRx and GOES BTs increased with time. Combined with the MBE, the FSS distinguished when changes in MAE were due to differences in the HRRRx BT bias or displacement in cloud features. The Method for Object-Based Diagnostic Evaluation (MODE) analyzed the similarity between HRRRx and GOES cloud features in shape and location. The similarity was summarized using the newly defined MODE composite score (MCS), an area-weighted calculation using the cloud feature match value from MODE. Combined with the FSS, the MCS indicated if HRRRx forecast error is the result of cloud shape, since the MCS is moderately large when forecast and observation objects are similar in size.

In this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-μm brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.

Abstract In this study, infrared brightness temperatures (BTs) are used to examine how applying stochastic perturbed parameter (SPP) methodology to the widely used Thompson–Eidhammer cloud microphysics scheme impacts the cloud field in high-resolution forecasts. Modifications are made to add stochastic perturbations to three parameters controlling cloud generation and dissipation processes. Two five-member ensembles are generated, one using the microphysics parameter perturbations (SPP-MP) and another where white noise perturbations were added to potential temperature fields at initialization time (Control). The impact of the SPP method was assessed using simulated and observed GOES-16 BTs. This analysis uses pixel-based and object-based methods to assess the impact on the cloud field. Pixel-based methods revealed that the SPP-MP BTs are slightly more accurate than the Control BTs. However, too few pixels with a BT lower than 270 K result in a positive bias compared to the observations. A negative bias compared to the observations is observed when only analyzing lower BTs. The spread of the ensemble BTs was analyzed using the continuous ranked probability score differences, with the SPP-MP ensemble BTs having less (more) spread during May (January) compared to the Control. Object-based analysis using the Method for Object-Based Diagnostic Evaluation revealed the upper-level cloud objects are smaller in the SPP-MP ensemble than the Control but a lower bias exists in the SPP-MP BTs compared to the Control BTs when overlapping matching objects. However, there is no clear distinction between the SPP-MP and Control ensemble members during the evolution of objects, Overall, the SPP-MP perturbations result in lower BTs compared to the Control ensemble and more cloudy pixels.

Infrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite‐16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper‐level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object‐Based Diagnostic Evaluation (MODE) and analyzed using the object‐based threat score, mean‐error distance, and pixel‐based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE‐identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS.